BackgroundGestational diabetes mellitus (GDM) is an increasingly prevalent risk factor for type 2 diabetes. We evaluated the effectiveness of a group-based lifestyle modification program in mothers with prior GDM within their first postnatal year.Methods and FindingsIn this study, 573 women were randomised to either the intervention (n = 284) or usual care (n = 289). At baseline, 10% had impaired glucose tolerance and 2% impaired fasting glucose. The diabetes prevention intervention comprised one individual session, five group sessions, and two telephone sessions. Primary outcomes were changes in diabetes risk factors (weight, waist circumference, and fasting blood glucose), and secondary outcomes included achievement of lifestyle modification goals and changes in depression score and cardiovascular disease risk factors. The mean changes (intention-to-treat [ITT] analysis) over 12 mo were as follows: −0.23 kg body weight in intervention group (95% CI −0.89, 0.43) compared with +0.72 kg in usual care group (95% CI 0.09, 1.35) (change difference −0.95 kg, 95% CI −1.87, −0.04; group by treatment interaction p = 0.04); −2.24 cm waist measurement in intervention group (95% CI −3.01, −1.42) compared with −1.74 cm in usual care group (95% CI −2.52, −0.96) (change difference −0.50 cm, 95% CI −1.63, 0.63; group by treatment interaction p = 0.389); and +0.18 mmol/l fasting blood glucose in intervention group (95% CI 0.11, 0.24) compared with +0.22 mmol/l in usual care group (95% CI 0.16, 0.29) (change difference −0.05 mmol/l, 95% CI −0.14, 0.05; group by treatment interaction p = 0.331). Only 10% of women attended all sessions, 53% attended one individual and at least one group session, and 34% attended no sessions. Loss to follow-up was 27% and 21% for the intervention and control groups, respectively, primarily due to subsequent pregnancies. Study limitations include low exposure to the full intervention and glucose metabolism profiles being near normal at baseline.ConclusionsAlthough a 1-kg weight difference has the potential to be significant for reducing diabetes risk, the level of engagement during the first postnatal year was low. Further research is needed to improve engagement, including participant involvement in study design; it is potentially more effective to implement annual diabetes screening until women develop prediabetes before offering an intervention.Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12610000338066

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Aims To describe the population distribution and socio‐economic position of residents across all states and territories of Australia, stratified using the 7 Modified Monash Model classifications. The numerical summary, and the methods described, can be applied by a variety of end users including workforce planners, researchers, policy‐makers and funding bodies for guiding future investment under different scenarios, and aid in evaluating geographically focused programs. Context The Commonwealth Department of Health is transitioning to the Modified Monash Model to objectively describe geographical access. This change applies to the Rural Health Multidisciplinary Training Program, one of the Australian Government's key policies to address the maldistribution of the rural health workforce. Unlike the previously applied Australian Statistical Geography Standard‐Remoteness Areas, a summary of the population in each Modified Monash Model classification is not available, nor is a socio‐economic overview of the communities within these areas. Approach Spatial analysis of Australian Bureau of Statistics data (Modified Monash Model, population data and the Index of Relative Socio‐economic Advantage and Disadvantage collected or derived from the 2016 census) at the Statistical Area 1—the smallest unit for the release of census data. Conclusion Linking the Modified Monash Model, a socio‐economic index and granular population data at the national level highlights the disadvantage of many residents in small rural towns (Modified Monash 5). The Modified Monash Model does not exhibit a continuum of the largest population residing in the most accessible classification and the smallest population residing in the least accessible classification that is seen in the Australian Statistical Geography Standard‐Remoteness Areas. Coupled with policy relevance, the advantage of using the Modified Monash Model as the basis for analysis is that it highlights areas that have both a critical mass of residents and differing levels of socio‐economic advantage and disadvantage. This will help end users to target funding to those regions where there is potential to improve access to services for the greatest number of rural residents.

Improved access to multibeam sonar and underwater video technology is enabling scientists to use spatially-explicit, predictive modelling to improve our understanding of marine ecosystems. With the growing number of modelling approaches available, knowledge of the relative performance of different models in the marine environment is required. Habitat suitability of 5 demersal fish taxa in Discovery Bay, south-east Australia, were modelled using 10 presence-only algorithms: BIOCLIM, DOMAIN, ENFA (distance geometric mean [GM], distance harmonic mean [HM], median [M], area-adjusted median [Ma], median + extremum [Me], area-adjusted median + extremum [Mae] and minimum distance [Min]), and MAXENT. Model performance was assessed using kappa and area under curve (AUC) of the receiver operator characteristic. The influence of spatial range (area of occupancy) and environmental niches (marginality and tolerance) on modelling performance were also tested. MAXENT generally performed best, followed by ENFA-GM and -HM, DOMAIN, BIO-CLIM, ENFA-M, -Min, -Ma, -Mae and -Me algorithms. Fish with clearly definable niches (i.e. high marginality) were most accurately modelled. Generally, Euclidean distance to nearest reef, HSI-b (backscatter), rugosity and maximum curvature were the most important variables in determining suitable habitat for the 5 demersal fish taxa investigated. This comparative study encourages ongoing use of presence-only approaches, particularly MAXENT, in modelling suitable habitat for demersal marine fishes. KEY WORDS: Species distribution modelling · Multibeam sonar · Towed-video · MAXENT · ENFA · BIOCLIM · DOMAIN Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 420: [157][158][159][160][161][162][163][164][165][166][167][168][169][170][171][172][173][174] 2010 Parallel to the development and application of species distribution modelling in the marine environment, is the increasing access to multibeam sonar (MBES) technology and underwater video systems. These technological developments, coupled with advances in geographic information systems and computational power, make it possible to survey large regions of seafloor with unprecedented accuracy and resolution (Nasby-Lucas et al. 2002, Iampietro et al. 2005, Wilson et al. 2007. MBES datasets are ideal for the application of a variety of terrainanalysis techniques, which form predictor variable datasets for input into models (see Wilson et al. 2007).While traditionally used for assessing sessile species, 'passive' underwater video systems such as drop video, towed/drift video and remotely operated vehicles (ROV) are increasingly being used as cost-effective, non-destructive methods for assessing marine fish species distributions (Morrison & Carbines 2006, Anderson & Yoklavich 2007. These video-based survey methods have significant advantages over traditional methods (e.g. SCUBA divers) in collecting fish occurrence data. They are capable of being deployed at depths and times that are dangerous for ...

ObjectiveGestational Diabetes Mellitus (GDM) increases the risk of type 2 diabetes. A register can be used to follow-up high risk women for early intervention to prevent progression to type 2 diabetes. We evaluate the performance of the world’s first national gestational diabetes register.Research design and methodsObservational study that used data linkage to merge: (1) pathology data from the Australian states of Victoria (VIC) and South Australia (SA); (2) birth records from the Consultative Council on Obstetric and Paediatric Mortality and Morbidity (CCOPMM, VIC) and the South Australian Perinatal Statistics Collection (SAPSC, SA); (3) GDM and type 2 diabetes register data from the National Gestational Diabetes Register (NGDR). All pregnancies registered on CCOPMM and SAPSC for 2012 and 2013 were included–other data back to 2008 were used to support the analyses. Rates of screening for GDM, rates of registration on the NGDR, and rates of follow-up laboratory screening for type 2 diabetes are reported.ResultsEstimated GDM screening rates were 86% in SA and 97% in VIC. Rates of registration on the NGDR ranged from 73% in SA (2013) to 91% in VIC (2013). During the study period rates of screening at six weeks postpartum ranged from 43% in SA (2012) to 58% in VIC (2013). There was little evidence of recall letters resulting in screening 12 months follow-up.ConclusionsGDM Screening and NGDR registration was effective in Australia. Recall by mail-out to young mothers and their GP’s for type 2 diabetes follow-up testing proved ineffective.